Triple
T27490686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York State Penal Law on the Thruway |
E693875
|
entity |
| Predicate | governsOffenseType |
P180198
|
FINISHED |
| Object | assault |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: assault | Statement: [New York State Penal Law on the Thruway, governsOffenseType, assault]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: governsOffenseType Context triple: [New York State Penal Law on the Thruway, governsOffenseType, assault]
-
A.
includesOffenseType
Indicates that one entity contains, specifies, or is associated with a particular category or type of offense.
-
B.
typeOfOffenseAddressed
chosen
Indicates the specific category or kind of offense that a given action, measure, or legal provision is intended to address.
-
C.
definesOffence
Indicates that one entity specifies or establishes the nature, elements, or scope of an offence associated with another entity.
-
D.
targetOffenderType
Indicates the specific category or type of offender that an action, rule, or condition is directed toward.
-
E.
typeOfLawEnforcement
Indicates that one entity is a specific kind or category of law enforcement associated with another entity.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ef5382b9648190be0b1ef2ad5d043c |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69fdb31800508190beec15adb9bbd292 |
completed | May 8, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69fdb19c381c8190bafb2f565da097f1 |
completed | May 8, 2026, 9:49 a.m. |
Created at: April 27, 2026, 1:05 p.m.